LLM-foundry update January 08, 2024 10:30:15

attention.py

@@ -1,15 +1,41 @@
 """Attention layers."""
 import math
 import warnings
-from typing import List, Optional, Tuple
+from typing import Any, Optional
 import torch
 import torch.nn as nn
+import transformers
 from einops import rearrange
 from packaging import version
 from torch import nn
 from .fc import FC_CLASS_REGISTRY
 from .norm import NORM_CLASS_REGISTRY
 
+def is_flash_v2_installed(v2_version: str='2.0.0'):
+    assert version.parse(v2_version) >= version.parse('2.0.0')
+    try:
+        import flash_attn as flash_attn
+    except:
+        return False
+    return version.parse(flash_attn.__version__) >= version.parse(v2_version)
+
+def is_flash_v1_installed():
+    try:
+        import flash_attn as flash_attn
+    except:
+        return False
+    return version.parse(flash_attn.__version__) < version.parse('2.0.0')
+
+def is_transformers_version_gte(hf_version: str) -> bool:
+    return version.parse(transformers.__version__) >= version.parse(hf_version)
+
+def check_alibi_support(attention_impl: str) -> bool:
+    return attention_impl != 'flash' or is_flash_v2_installed(v2_version='v2.4.2')
+if is_flash_v1_installed():
+    import transformers
+    transformers.utils.is_flash_attn_available = lambda : False
+from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
+
 def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool) -> bool:
     if original_is_causal and num_query_tokens != num_key_tokens:
         if num_query_tokens != 1:
@@ -18,7 +44,20 @@ def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_cau
             return False
     return original_is_causal
 
-def scaled_multihead_dot_product_attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+def repeat_kv_for_gqa(hidden: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """Perform repeat of kv heads along a particular dimension.
+
+    hidden.shape expected to be: (batch size, seq len, kv_n_heads, head_dim)
+    n_rep: amount of repetitions of kv_n_heads
+    Unlike torch.repeat_interleave, this function avoids allocating new memory.
+    """
+    if n_rep == 1:
+        return hidden
+    (b, s, kv_n_heads, d) = hidden.shape
+    hidden = hidden[:, :, :, None, :].expand(b, s, kv_n_heads, n_rep, d)
+    return hidden.reshape(b, s, kv_n_heads * n_rep, d)
+
+def scaled_multihead_dot_product_attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]]:
     if multiquery:
         warnings.warn(DeprecationWarning('The direct use of the multiquery arg is deprecated. Setting kv_n_heads=1 automatically. Please set kv_n_heads=1 explicitly to remove this warning.'))
         kv_n_heads = 1
@@ -36,8 +75,8 @@ def scaled_multihead_dot_product_attention(query: torch.Tensor, key: torch.Tenso
     (b, _, s_q, d) = q.shape
     s_k = k.size(-1)
     if kv_n_heads > 1 and kv_n_heads < n_heads:
-        k = k.repeat_interleave(n_heads // kv_n_heads, dim=1)
-        v = v.repeat_interleave(n_heads // kv_n_heads, dim=1)
+        k = repeat_kv_for_gqa(k.transpose(1, 2), n_heads // kv_n_heads).transpose(1, 2)
+        v = repeat_kv_for_gqa(v.transpose(1, 2), n_heads // kv_n_heads).transpose(1, 2)
     if softmax_scale is None:
         softmax_scale = 1 / math.sqrt(d)
     attn_weight = q.matmul(k) * softmax_scale
@@ -70,7 +109,7 @@ def scaled_multihead_dot_product_attention(query: torch.Tensor, key: torch.Tenso
         return (out, attn_weight, past_key_value)
     return (out, None, past_key_value)
 
-def check_valid_inputs(*tensors: torch.Tensor, valid_dtypes: Optional[List[torch.dtype]]=None):
+def check_valid_inputs(*tensors: torch.Tensor, valid_dtypes: Optional[list[torch.dtype]]=None):
     if valid_dtypes is None:
         valid_dtypes = [torch.float16, torch.bfloat16]
     for tensor in tensors:
@@ -79,11 +118,11 @@ def check_valid_inputs(*tensors: torch.Tensor, valid_dtypes: Optional[List[torch
         if not tensor.is_cuda:
             raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).')
 
-def flash_attn_fn(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+def flash_attn_fn(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False, attention_mask_in_length: Optional[torch.Tensor]=None, should_repeat_kv_for_gqa: Optional[bool]=True, sliding_window_size: int=-1, alibi_slopes: Optional[torch.Tensor]=None) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]]:
     try:
         from flash_attn import bert_padding, flash_attn_interface
     except:
-        raise RuntimeError('Please install flash-attn==1.0.3.post0')
+        raise RuntimeError('Please install flash-attn==1.0.9 or flash-attn==2.3.6')
     check_valid_inputs(query, key, value)
     if multiquery:
         warnings.warn(DeprecationWarning('The direct use of the multiquery arg is deprecated. Setting kv_n_heads=1 automatically. Please set kv_n_heads=1 explicitly to remove this warning.'))
@@ -96,35 +135,50 @@ def flash_attn_fn(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n
             key = torch.cat([past_key_value[0], key], dim=1)
             value = torch.cat([past_key_value[1], value], dim=1)
         past_key_value = (key, value)
-    if attn_bias is not None:
-        _s_q = max(0, attn_bias.size(2) - query.size(1))
-        _s_k = max(0, attn_bias.size(3) - key.size(1))
-        attn_bias = attn_bias[:, :, _s_q:, _s_k:]
     if attn_bias is not None:
         raise NotImplementedError(f'attn_bias not implemented for flash attn.')
     (batch_size, seqlen) = query.shape[:2]
-    if key_padding_mask is None:
-        key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool)
-    query_padding_mask = key_padding_mask[:, -query.size(1):]
-    (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask)
+    if attention_mask_in_length is None:
+        if key_padding_mask is None:
+            key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool)
+        query_padding_mask = key_padding_mask[:, -query.size(1):]
+        unpadding_function = bert_padding.unpad_input
+    else:
+        key_padding_mask = attention_mask_in_length
+        query_padding_mask = attention_mask_in_length
+        unpadding_function = bert_padding.unpad_input_for_concatenated_sequences
+    (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = unpadding_function(query, query_padding_mask)
     query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads)
-    (key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask)
+    (key_unpad, _, cu_seqlens_k, max_seqlen_k) = unpadding_function(key, key_padding_mask)
     key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=kv_n_heads)
-    (value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask)
+    (value_unpad, _, _, _) = unpadding_function(value, key_padding_mask)
     value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=kv_n_heads)
-    if kv_n_heads == 1:
-        key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1))
-        value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1))
-    elif kv_n_heads < n_heads:
-        key_unpad = key_unpad.repeat_interleave(n_heads // kv_n_heads, dim=1)
-        value_unpad = value_unpad.repeat_interleave(n_heads // kv_n_heads, dim=1)
+    if kv_n_heads < n_heads and (not is_flash_v2_installed()) and (not should_repeat_kv_for_gqa):
+        raise ValueError('For Grouped Query Attention or Multi Query Attention, should_repeat_kv_for_gqa should be set to True if not using Flash Attention v2.')
+    if should_repeat_kv_for_gqa:
+        if kv_n_heads == 1:
+            key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1))
+            value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1))
+        elif kv_n_heads < n_heads:
+            key_unpad = repeat_kv_for_gqa(key_unpad.view(1, key_unpad.size(0), kv_n_heads, -1), n_heads // kv_n_heads).view(key_unpad.size(0), n_heads, -1)
+            value_unpad = repeat_kv_for_gqa(value_unpad.view(1, value_unpad.size(0), kv_n_heads, -1), n_heads // kv_n_heads).view(value_unpad.size(0), n_heads, -1)
     dropout_p = dropout_p if training else 0.0
     reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
-    output_unpad = flash_attn_interface.flash_attn_unpadded_func(query_unpad, key_unpad, value_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights)
+    if is_flash_v1_installed():
+        output_unpad = flash_attn_interface.flash_attn_unpadded_func(q=query_unpad, k=key_unpad, v=value_unpad, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_q, max_seqlen_k=max_seqlen_k, dropout_p=dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights)
+    elif is_flash_v2_installed():
+        alibi_kwargs = {}
+        if check_alibi_support('flash'):
+            alibi_kwargs = {'alibi_slopes': alibi_slopes}
+        elif alibi_slopes is not None:
+            raise ValueError('alibi_slopes is only supported for flash-attn>=2.4.2')
+        output_unpad = flash_attn_interface.flash_attn_varlen_func(q=query_unpad, k=key_unpad, v=value_unpad, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_q, max_seqlen_k=max_seqlen_k, dropout_p=dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights, window_size=(sliding_window_size, sliding_window_size), **alibi_kwargs)
+    else:
+        raise RuntimeError('flash-attn==1.0.9 or flash-attn==2.4.2 is required.')
     output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen)
     return (output, None, past_key_value)
 
-def triton_flash_attn_fn(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+def triton_flash_attn_fn(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]]:
     try:
         from .flash_attn_triton import flash_attn_func
     except:
@@ -171,8 +225,8 @@ def triton_flash_attn_fn(query: torch.Tensor, key: torch.Tensor, value: torch.Te
         key = key.repeat(1, 1, n_heads, 1)
         value = value.repeat(1, 1, n_heads, 1)
     elif kv_n_heads < n_heads:
-        key = key.repeat_interleave(n_heads // kv_n_heads, dim=2)
-        value = value.repeat_interleave(n_heads // kv_n_heads, dim=2)
+        key = repeat_kv_for_gqa(key, n_heads // kv_n_heads)
+        value = repeat_kv_for_gqa(value, n_heads // kv_n_heads)
     reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
     attn_output = flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale)
     output = attn_output.view(*attn_output.shape[:2], -1)
@@ -188,7 +242,7 @@ class GroupedQueryAttention(nn.Module):
     implementation enables user to also use additive bias.
     """
 
-    def __init__(self, d_model: int, n_heads: int, kv_n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None):
+    def __init__(self, d_model: int, n_heads: int, kv_n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None, bias: bool=True, sliding_window_size: int=-1):
         super().__init__()
         self.attn_impl = attn_impl
         self.clip_qkv = clip_qkv
@@ -196,6 +250,7 @@ class GroupedQueryAttention(nn.Module):
         self.d_model = d_model
         self.n_heads = n_heads
         self.kv_n_heads = kv_n_heads
+        self.sliding_window_size = sliding_window_size
         self.head_dim = d_model // n_heads
         if self.kv_n_heads <= 0:
             raise ValueError('kv_n_heads should be greater than zero.')
@@ -207,7 +262,7 @@ class GroupedQueryAttention(nn.Module):
         if self.softmax_scale is None:
             self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads)
         self.attn_dropout_p = attn_pdrop
-        fc_kwargs = {}
+        fc_kwargs: dict[str, Any] = {'bias': bias}
         if fc_type != 'te':
             fc_kwargs['device'] = device
         self.Wqkv = FC_CLASS_REGISTRY[fc_type](self.d_model, self.d_model + 2 * self.kv_n_heads * self.head_dim, **fc_kwargs)
@@ -228,7 +283,7 @@ class GroupedQueryAttention(nn.Module):
         self.out_proj = FC_CLASS_REGISTRY[fc_type](self.d_model, self.d_model, **fc_kwargs)
         self.out_proj._is_residual = True
 
-    def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, is_causal: bool=True, needs_weights: bool=False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+    def forward(self, x: torch.Tensor, past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, rotary_emb_w_meta_info: Optional[dict]=None, is_causal: bool=True, needs_weights: bool=False, attention_mask_in_length: Optional[torch.Tensor]=None, alibi_slopes: Optional[torch.Tensor]=None) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]]:
         qkv = self.Wqkv(x)
         if self.clip_qkv:
             qkv = qkv.clamp(min=-self.clip_qkv, max=self.clip_qkv)
@@ -238,7 +293,35 @@ class GroupedQueryAttention(nn.Module):
             dtype = query.dtype
             query = self.q_ln(query).to(dtype)
             key = self.k_ln(key).to(dtype)
-        (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, self.kv_n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights)
+        if rotary_emb_w_meta_info is not None:
+            rotary_emb = rotary_emb_w_meta_info['rotary_emb']
+            seq_len = rotary_emb_w_meta_info['seq_len']
+            offset_info = rotary_emb_w_meta_info['offset_info']
+            (bsz, seqlen) = query.shape[:2]
+            query = query.view(bsz, seqlen, -1, self.head_dim)
+            key = key.view(bsz, seqlen, -1, self.head_dim)
+            if rotary_emb_w_meta_info['impl'] == 'dail':
+                value = value.view(bsz, seqlen, -1, self.head_dim)
+                kv = torch.stack([key, value], dim=2)
+                (query, kv) = rotary_emb(query, kv, seqlen_offset=offset_info, max_seqlen=seq_len)
+                [key, value] = torch.unbind(kv, dim=2)
+                value = value.view(bsz, seqlen, self.kv_n_heads * self.head_dim)
+            elif rotary_emb_w_meta_info['impl'] == 'hf':
+                (cos, sin) = rotary_emb(value, seq_len)
+                if is_transformers_version_gte('4.36'):
+                    (query, key) = apply_rotary_pos_emb(query, key, cos, sin, offset_info, unsqueeze_dim=2)
+                else:
+                    query = query.transpose(1, 2)
+                    key = key.transpose(1, 2)
+                    (query, key) = apply_rotary_pos_emb(query, key, cos, sin, offset_info)
+                    query = query.transpose(1, 2)
+                    key = key.transpose(1, 2)
+            query = query.view(bsz, seqlen, self.d_model)
+            key = key.view(bsz, seqlen, self.kv_n_heads * self.head_dim)
+        extra_attn_kwargs = {}
+        if self.attn_impl == 'flash':
+            extra_attn_kwargs = {'attention_mask_in_length': attention_mask_in_length, 'should_repeat_kv_for_gqa': not is_flash_v2_installed(), 'sliding_window_size': self.sliding_window_size, 'alibi_slopes': alibi_slopes}
+        (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, self.kv_n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights, **extra_attn_kwargs)
         return (self.out_proj(context), attn_weights, past_key_value)
 
 class MultiheadAttention(GroupedQueryAttention):
@@ -248,8 +331,8 @@ class MultiheadAttention(GroupedQueryAttention):
     additive bias.
     """
 
-    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None):
-        super().__init__(d_model=d_model, n_heads=n_heads, kv_n_heads=n_heads, attn_impl=attn_impl, clip_qkv=clip_qkv, qk_ln=qk_ln, softmax_scale=softmax_scale, attn_pdrop=attn_pdrop, norm_type=norm_type, fc_type=fc_type, device=device)
+    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None, bias: bool=True, sliding_window_size: int=-1):
+        super().__init__(d_model=d_model, n_heads=n_heads, kv_n_heads=n_heads, attn_impl=attn_impl, clip_qkv=clip_qkv, qk_ln=qk_ln, softmax_scale=softmax_scale, attn_pdrop=attn_pdrop, norm_type=norm_type, fc_type=fc_type, device=device, bias=bias, sliding_window_size=sliding_window_size)
 
 class MultiQueryAttention(GroupedQueryAttention):
     """Multi-Query self attention.
@@ -258,10 +341,10 @@ class MultiQueryAttention(GroupedQueryAttention):
     additive bias.
     """
 
-    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None):
-        super().__init__(d_model=d_model, n_heads=n_heads, kv_n_heads=1, attn_impl=attn_impl, clip_qkv=clip_qkv, qk_ln=qk_ln, softmax_scale=softmax_scale, attn_pdrop=attn_pdrop, norm_type=norm_type, fc_type=fc_type, device=device)
+    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None, bias: bool=True, sliding_window_size: int=-1):
+        super().__init__(d_model=d_model, n_heads=n_heads, kv_n_heads=1, attn_impl=attn_impl, clip_qkv=clip_qkv, qk_ln=qk_ln, softmax_scale=softmax_scale, attn_pdrop=attn_pdrop, norm_type=norm_type, fc_type=fc_type, device=device, bias=bias, sliding_window_size=sliding_window_size)
 
-def attn_bias_shape(attn_impl: str, n_heads: int, seq_len: int, alibi: bool, prefix_lm: bool, causal: bool, use_sequence_id: bool) -> Optional[Tuple[int, int, int, int]]:
+def attn_bias_shape(attn_impl: str, n_heads: int, seq_len: int, alibi: bool, prefix_lm: bool, causal: bool, use_sequence_id: bool) -> Optional[tuple[int, int, int, int]]:
     if attn_impl == 'flash':
         return None
     elif attn_impl in ['torch', 'triton']:
@@ -286,13 +369,15 @@ def build_attn_bias(attn_impl: str, attn_bias: torch.Tensor, n_heads: int, seq_l
     else:
         raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
 
-def gen_slopes(n_heads: int, alibi_bias_max: int=8, device: Optional[torch.device]=None) -> torch.Tensor:
+def gen_slopes(n_heads: int, alibi_bias_max: int=8, device: Optional[torch.device]=None, return_1d: bool=False) -> torch.Tensor:
     _n_heads = 2 ** math.ceil(math.log2(n_heads))
     m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device)
     m = m.mul(alibi_bias_max / _n_heads)
     slopes = 1.0 / torch.pow(2, m)
     if _n_heads != n_heads:
         slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads]
+    if return_1d:
+        return slopes
     return slopes.view(1, n_heads, 1, 1)
 
 def build_alibi_bias(n_heads: int, seq_len: int, full: bool=False, alibi_bias_max: int=8, device: Optional[torch.device]=None, dtype: Optional[torch.dtype]=None) -> torch.Tensor:

blocks.py

@@ -5,12 +5,17 @@ import torch.nn as nn
 from .attention import ATTN_CLASS_REGISTRY
 from .ffn import FFN_CLASS_REGISTRY, build_ffn
 from .norm import NORM_CLASS_REGISTRY
+try:
+    from flash_attn.bert_padding import unpad_input, pad_input
+except:
+    (unpad_input, pad_input) = (None, None)
+attn_config_defaults: Dict = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'sliding_window_size': -1, 'alibi': False, 'alibi_bias_max': 8, 'rope': False, 'rope_theta': 10000, 'rope_impl': 'dail', 'rope_dail_config': {'type': 'original', 'pos_idx_in_fp32': True, 'xpos_scale_base': 512}, 'rope_hf_config': {'type': 'no_scaling', 'factor': 1.0}}
 
 class MPTBlock(nn.Module):
 
-    def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, attn_config: Optional[Dict]=None, ffn_config: Optional[Dict]=None, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None, **kwargs: Any):
+    def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, attn_config: Optional[Dict]=None, ffn_config: Optional[Dict]=None, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None, no_bias: bool=False, use_pad_tok_in_ffn: bool=True, **kwargs: Any):
         if attn_config is None:
-            attn_config = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}
+            attn_config = attn_config_defaults
         if ffn_config is None:
             ffn_config = {'ffn_type': 'mptmlp'}
         del kwargs
@@ -18,24 +23,33 @@ class MPTBlock(nn.Module):
         norm_class = NORM_CLASS_REGISTRY[norm_type.lower()]
         assert isinstance(attn_config['attn_type'], str)
         attn_class = ATTN_CLASS_REGISTRY[attn_config['attn_type']]
-        args_to_exclude_in_attn_class = {'attn_type', 'prefix_lm', 'alibi', 'attn_uses_sequence_id', 'alibi_bias_max'}
+        args_to_exclude_in_attn_class = {'attn_type', 'prefix_lm', 'alibi', 'attn_uses_sequence_id', 'alibi_bias_max', 'rope', 'rope_theta', 'rope_impl', 'rope_dail_config', 'rope_hf_config'}
         attn_config_subset_for_attn_class = {k: v for (k, v) in attn_config.items() if k not in args_to_exclude_in_attn_class}
         self.norm_1 = norm_class(d_model, device=device)
-        self.attn = attn_class(d_model=d_model, n_heads=n_heads, fc_type=fc_type, device=device, **attn_config_subset_for_attn_class)
+        self.attn = attn_class(d_model=d_model, n_heads=n_heads, fc_type=fc_type, device=device, **attn_config_subset_for_attn_class, bias=not no_bias)
         self.norm_2 = None
         if not getattr(FFN_CLASS_REGISTRY[ffn_config['ffn_type']], '_has_norm', False):
             self.norm_2 = norm_class(d_model, device=device)
-        self.ffn = build_ffn(d_model=d_model, expansion_ratio=expansion_ratio, device=device, **ffn_config)
+        self.ffn = build_ffn(d_model=d_model, expansion_ratio=expansion_ratio, device=device, bias=not no_bias, **ffn_config)
         self.resid_attn_dropout = nn.Dropout(resid_pdrop)
         self.resid_ffn_dropout = nn.Dropout(resid_pdrop)
+        self.use_pad_tok_in_ffn = use_pad_tok_in_ffn
 
-    def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
+    def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, rotary_emb_w_meta_info: Optional[Dict]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True, output_attentions: bool=False, attention_mask_in_length: Optional[torch.Tensor]=None, alibi_slopes: Optional[torch.Tensor]=None) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
         a = self.norm_1(x)
-        (b, attn_weights, past_key_value) = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=is_causal)
+        (b, attn_weights, past_key_value) = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, rotary_emb_w_meta_info=rotary_emb_w_meta_info, attention_mask=attention_mask, is_causal=is_causal, needs_weights=output_attentions, attention_mask_in_length=attention_mask_in_length, alibi_slopes=alibi_slopes)
         x = x + self.resid_attn_dropout(b)
         m = x
         if self.norm_2 is not None:
             m = self.norm_2(x)
+        (batch_size, seq_len) = m.size()[:2]
+        indices = None
+        if not self.use_pad_tok_in_ffn:
+            assert unpad_input is not None
+            (m, indices, _, _) = unpad_input(m, attention_mask)
         n = self.ffn(m)
+        if not self.use_pad_tok_in_ffn:
+            assert pad_input is not None
+            n = pad_input(n, indices, batch_size, seq_len)
         x = x + self.resid_ffn_dropout(n)
         return (x, attn_weights, past_key_value)

configuration_mpt.py

@@ -2,21 +2,25 @@
 import warnings
 from typing import Any, Dict, Optional, Union
 from transformers import PretrainedConfig
-attn_config_defaults: Dict = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}
+from .attention import check_alibi_support, is_flash_v2_installed
+from .blocks import attn_config_defaults
+from .fc import FC_CLASS_REGISTRY
+from .norm import LPLayerNorm
+from .ffn import FFN_CLASS_REGISTRY
 ffn_config_defaults: Dict = {'ffn_type': 'mptmlp'}
 init_config_defaults: Dict = {'name': 'kaiming_normal_', 'fan_mode': 'fan_in', 'init_nonlinearity': 'relu', 'init_div_is_residual': True, 'emb_init_std': None, 'emb_init_uniform_lim': None, 'init_std': None, 'init_gain': 0.0}
 
 class MPTConfig(PretrainedConfig):
     model_type = 'mpt'
 
-    def __init__(self, d_model: int=2048, n_heads: int=16, n_layers: int=24, expansion_ratio: int=4, max_seq_len: int=2048, vocab_size: int=50368, resid_pdrop: float=0.0, emb_pdrop: float=0.0, learned_pos_emb: bool=True, attn_config: Dict=attn_config_defaults, ffn_config: Dict=ffn_config_defaults, init_device: str='cpu', logit_scale: Optional[Union[float, str]]=None, no_bias: bool=False, embedding_fraction: float=1.0, norm_type: str='low_precision_layernorm', use_cache: bool=False, init_config: Dict=init_config_defaults, fc_type: str='torch', verbose: Optional[int]=None, **kwargs: Any):
+    def __init__(self, d_model: int=2048, n_heads: int=16, n_layers: int=24, expansion_ratio: Union[int, float]=4, max_seq_len: int=2048, vocab_size: int=50368, resid_pdrop: float=0.0, emb_pdrop: float=0.0, learned_pos_emb: bool=True, attn_config: Dict=attn_config_defaults, ffn_config: Dict=ffn_config_defaults, init_device: str='cpu', logit_scale: Optional[Union[float, str]]=None, no_bias: bool=False, embedding_fraction: float=1.0, norm_type: str='low_precision_layernorm', use_cache: bool=False, init_config: Dict=init_config_defaults, fc_type: str='torch', tie_word_embeddings: bool=True, use_pad_tok_in_ffn: bool=True, verbose: Optional[int]=None, **kwargs: Any):
         """The MPT configuration class.
 
         Args:
             d_model (int): The size of the embedding dimension of the model.
             n_heads (int): The number of attention heads.
             n_layers (int): The number of layers in the model.
-            expansion_ratio (int): The ratio of the up/down scale in the ffn.
+            expansion_ratio (Union[int, float]): The ratio of the up/down scale in the ffn.
             max_seq_len (int): The maximum sequence length of the model.
             vocab_size (int): The size of the vocabulary.
             resid_pdrop (float): The dropout probability applied to the attention output before combining with residual.
@@ -38,15 +42,26 @@ class MPTConfig(PretrainedConfig):
                     When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates
                     which sub-sequence each token belongs to.
                     Defaults to ``False`` meaning any provided `sequence_id` will be ignored.
+                sliding_window_size (int): Window size for sliding window local attention. Defaults to -1, which means no sliding window. Query at position i will only attend to keys between [i + seqlen_k - seqlen_q - window_size, i + seqlen_k - seqlen_q + window_size] inclusive. Only works for flash attention v2.3.0 or higher.
                 alibi (bool): Whether to use the alibi bias instead of position embeddings.
                 alibi_bias_max (int): The maximum value of the alibi bias.
+                rope (bool): Whether to use rotary positional embeddings.
+                rope_theta (int): The base frequency for rope.
+                rope_impl (str): The implementation of rope to use. One of 'hf' (to use the implementation from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py) or 'dail' (to use the implementation from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py).
+                rope_dail_config (Dict): The configuration for the dail implementation of rope.
+                    type (str): The type of rotary position embedding to use. Options: 'original' (for https://arxiv.org/pdf/2104.09864.pdf), 'xpos' (for https://arxiv.org/pdf/2212.10554.pdf).
+                    pos_idx_in_fp32 (bool): If True, the position indices [0, ..., seqlen - 1] are in fp32, otherwise they might be in lower precision. A consequence could be, for example, that bf16 rounds position 1995 to 2000, which leads to them having the same positional embedding.
+                    xpos_scale_base (float): The scale base for XPos (if using XPos).
+                rope_hf_config (Dict): A dictionary used to configure rope's scaling behavior (when scaling beyond the training length).
+                    type (str): Can be one of 'no_scaling', 'linear', or 'dynamic'. 'no_scaling' uses the default implementation for rotary embeddings, 'linear' uses linear scaling as proposed by the Reddit user /u/kaiokendev, and 'dynamic' uses Dynamic NTK scaling as proposed by the Reddit users /u/bloc97 and /u/emozilla.
+                    factor (float): Scaling factor to use if using 'linear' or 'dynamic' as rope_scaling.type.
                 kv_n_heads (Optional[int]): For grouped_query_attention only, allow user to specify number of kv heads.
             ffn_config (Dict): A dictionary used to configure the model's ffn module:
-                ffn_type (str): type of ffn to use. Options: mptmlp, te_ln_mlp
+                ffn_type (str): type of ffn to use. Options: mptmlp, mptglu, te_ln_mlp
             init_device (str): The device to use for parameter initialization.
             logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value.
             no_bias (bool): Whether to use bias in all layers.
-            verbose (int): The verbosity level. 0 is silent.
+            verbose (int): Deprecated.
             embedding_fraction (float): The fraction to scale the gradients of the embedding layer by.
             norm_type (str): choose type of norm to use
             use_cache (bool): Whether or not the model should return the last key/values attentions
@@ -66,6 +81,8 @@ class MPTConfig(PretrainedConfig):
                 ---
                 See llmfoundry.models.utils.param_init_fns.py for info on other param init config options
             fc_type (str): choose fc layer implementation. Options: torch and te. te layers support fp8 when using H100 GPUs.
+            tie_word_embeddings (bool): Whether to tie the input embedding and output layers.
+            use_pad_tok_in_ffn (bool): Whether to forward the pad token in the feedforward networks.
         """
         self.d_model = d_model
         self.n_heads = n_heads
@@ -86,22 +103,25 @@ class MPTConfig(PretrainedConfig):
         self.use_cache = use_cache
         self.init_config = init_config
         self.fc_type = fc_type
+        self.use_pad_tok_in_ffn = use_pad_tok_in_ffn
         if verbose is not None:
             warnings.warn(DeprecationWarning('verbose argument for MPTConfig is now ignored and will be removed. Use python_log_level instead.'))
         if 'name' in kwargs:
             del kwargs['name']
         if 'loss_fn' in kwargs:
             del kwargs['loss_fn']
-        if self.attn_config.get('alibi', False):
+        if self.attn_config.get('alibi', False) or self.attn_config.get('rope', False):
             self.learned_pos_emb = False
-            warnings.warn(f'alibi is turned on, setting `learned_pos_emb` to `False.`')
-        super().__init__(**kwargs)
+            warnings.warn(f'alibi or rope is turned on, setting `learned_pos_emb` to `False.`')
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
         self._validate_config()
 
     def _set_config_defaults(self, config: Dict[str, Any], config_defaults: Dict[str, Any]) -> Dict[str, Any]:
         for (k, v) in config_defaults.items():
             if k not in config:
                 config[k] = v
+            elif isinstance(v, dict):
+                config[k] = self._set_config_defaults(config[k] if config[k] is not None else {}, v)
         return config
 
     def _validate_config(self) -> None:
@@ -116,25 +136,45 @@ class MPTConfig(PretrainedConfig):
             raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}")
         if self.attn_config['prefix_lm'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
             raise NotImplementedError('prefix_lm only implemented with torch and triton attention.')
-        if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
-            raise NotImplementedError('alibi only implemented with torch and triton attention.')
-        if self.attn_config['attn_uses_sequence_id'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
-            raise NotImplementedError('attn_uses_sequence_id only implemented with torch and triton attention.')
+        if self.attn_config['alibi'] and (not check_alibi_support(self.attn_config['attn_impl'])):
+            raise NotImplementedError('alibi only implemented with torch, triton, and flash (v2.4.2 or higher) attention.')
+        if self.attn_config['attn_uses_sequence_id'] and (not (self.attn_config['attn_impl'] in ['torch', 'triton'] or (self.attn_config['attn_impl'] == 'flash' and is_flash_v2_installed(v2_version='v2.1.2')))):
+            raise NotImplementedError('attn_uses_sequence_id only implemented with torch, triton, and flash (v2.1.2 or higher) attention.')
+        if self.attn_config['rope'] and self.attn_config['rope_impl'] not in ['dail', 'hf']:
+            raise ValueError('If rope is being used then rope_impl should be either "dail", or "hf".')
+        if self.attn_config['rope'] and self.attn_config['rope_impl'] == 'hf' and (self.attn_config['rope_hf_config']['type'] not in ['no_scaling', 'linear', 'dynamic']):
+            raise ValueError('If using hf implementation of rope, the type should be one of "no_scaling", "linear" or "dynamic".')
+        if self.attn_config['rope'] and self.attn_config['rope_impl'] == 'dail':
+            if self.attn_config['rope_dail_config']['type'] not in ['original', 'xpos']:
+                raise ValueError('If using the dail implementation of rope, the type should be one of "original" or "xpos".')
+            if not is_flash_v2_installed(v2_version='2.0.1'):
+                raise ImportError('If using the dail implementation of rope, the flash_attn library v2.0.1 or higher must be installed. Please check the instructions at https://github.com/mosaicml/llm-foundry/blob/main/TUTORIAL.md#what-kinds-of-positional-embeddings-does-llm-foundry-support')
+        if self.attn_config['sliding_window_size'] != -1 and (not (self.attn_config['attn_impl'] == 'flash' and is_flash_v2_installed(v2_version='v2.3.0'))):
+            raise NotImplementedError('sliding window only implemented with flash attention v2.3.0 or higher.')
         if self.embedding_fraction > 1 or self.embedding_fraction <= 0:
             raise ValueError('model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!')
         if isinstance(self.logit_scale, str) and self.logit_scale != 'inv_sqrt_d_model':
             raise ValueError(f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
         if self.init_config.get('name', None) is None:
             raise ValueError(f"self.init_config={self.init_config!r} 'name' needs to be set.")
-        if not self.learned_pos_emb and (not self.attn_config['alibi']):
-            warnings.warn(f'Positional information not being provided to the model using either learned_pos_emb or alibi.')
+        if not (self.learned_pos_emb or self.attn_config['alibi'] or self.attn_config['rope']):
+            warnings.warn(f'Positional information not being provided to the model using either learned_pos_emb or alibi or rope.')
         if self.fc_type == 'te' or self.ffn_config['ffn_type'] == 'te_ln_mlp':
             try:
                 import transformer_engine.pytorch as te
                 del te
             except:
                 raise ImportError('TransformerEngine import fail. `fc_type: te` requires TransformerEngine be installed. ' + 'The required version of transformer_engine also requires FlashAttention v1.0.6 is installed:\n' + 'pip install flash-attn==1.0.6 --no-build-isolation \n' + 'pip install git+https://github.com/NVIDIA/TransformerEngine.git@144e4888b2cdd60bd52e706d5b7a79cb9c1a7156')
-        if self.ffn_config['ffn_type'] == 'mptmlp':
+        if self.ffn_config['ffn_type'] == 'mptgeglu':
+            raise ValueError('API CHANGE: `ffn_type=="mptgeglu"` changed to `ffn_type=="mptglu"`. ' + 'See [#829](https://github.com/mosaicml/llm-foundry/pull/829) for details.')
+        elif self.ffn_config['ffn_type'] in ['mptmlp', 'mptglu']:
             self.ffn_config['fc_type'] = self.fc_type
         elif self.ffn_config['ffn_type'] == 'te_ln_mlp':
-            self.ffn_config['bias'] = not self.no_bias
+            self.ffn_config['bias'] = not self.no_bias
+            if 'ffn_act_fn' in self.ffn_config.keys():
+                raise ValueError(f'Transformer Engine block does not support custom activation functions.')
+        if not self.use_pad_tok_in_ffn:
+            try:
+                from flash_attn.bert_padding import unpad_input, pad_input
+            except:
+                raise ImportError('In order to set `use_pad_tok_in_ffn=False`, please install flash-attn==1.0.9 or flash-attn==2.3.6')

ffn.py

@@ -1,5 +1,8 @@
-"""GPT Blocks used for the GPT Model."""
-from typing import Any, Optional
+"""MPT Blocks used for the MPT Model."""
+import logging
+from copy import deepcopy
+from functools import partial
+from typing import Any, Callable, Optional, Union
 import torch
 import torch.nn as nn
 from .fc import FC_CLASS_REGISTRY
@@ -7,33 +10,88 @@ try:
     import transformer_engine.pytorch as te
 except:
     te = None
+log = logging.getLogger(__name__)
+_FFN_ACT_FN_DEFAULT = {'name': 'gelu', 'approximate': 'none'}
+
+def resolve_ffn_act_fn(config: Optional[dict]=None) -> Callable[[torch.Tensor], torch.Tensor]:
+    """Resolve the activation function for the feed-forward network.
+
+    Args:
+        config (Optional[dict]): The configuration dictionary for the activation function.
+            The dict config must specify the 'name' of a torch.nn.functional activation
+            function. All of other key values pairs are bound to the function as a partial.
+
+    Returns:
+        Callable[[torch.Tensor], torch.Tensor]: The activation function.
+    """
+    if config is None:
+        config = _FFN_ACT_FN_DEFAULT
+    config = deepcopy(config)
+    name = config.pop('name')
+    if not hasattr(torch.nn.functional, name):
+        raise ValueError(f'Unrecognised activation function name ({name}).')
+    act = getattr(torch.nn.functional, name)
+    return partial(act, **config)
+_DEFAULT_ACT_FN = resolve_ffn_act_fn(_FFN_ACT_FN_DEFAULT)
+
+def resolve_ffn_hidden_size(d_model: int, expansion_ratio: Union[int, float], ffn_hidden_size: Optional[int]=None) -> int:
+    """Resolve the hidden size of the feed-forward network.
+
+    Args:
+        d_model (int): The dimension of the input and output of the feed-forward network.
+        expansion_ratio (Union[int, float]): The expansion ratio of the feed-forward network.
+        ffn_hidden_size (Optional[int]): The hidden size of the feed-forward network.
+
+    Returns:
+        int: The hidden size of the feed-forward network.
+    """
+    if ffn_hidden_size is not None:
+        log.info(f'`expansion_ratio` (={expansion_ratio}) ignored when `ffn_hidden_size` (={ffn_hidden_size}) is specified.')
+    else:
+        ffn_hidden_size = int(d_model * expansion_ratio)
+        if ffn_hidden_size != d_model * expansion_ratio:
+            raise ValueError(f'`d_model * expansion_ratio` must be an integer (d_model={d_model!r}; expansion_ratio={expansion_ratio!r}; d_model * expansion_ratio={d_model * expansion_ratio!r}).')
+    return ffn_hidden_size
 
 class MPTMLP(nn.Module):
 
-    def __init__(self, d_model: int, expansion_ratio: int, fc_type: str='torch', device: Optional[str]=None):
+    def __init__(self, d_model: int, expansion_ratio: Union[int, float], fc_type: str='torch', ffn_hidden_size: Optional[int]=None, act_fn: Callable[[torch.Tensor], torch.Tensor]=_DEFAULT_ACT_FN, device: Optional[str]=None, bias: bool=True):
         super().__init__()
-        fc_kwargs = {}
+        ffn_hidden_size = resolve_ffn_hidden_size(d_model, expansion_ratio, ffn_hidden_size)
+        self.fc_kwargs: dict[str, Any] = {'bias': bias}
         if fc_type != 'te':
-            fc_kwargs['device'] = device
-        self.up_proj = FC_CLASS_REGISTRY[fc_type](d_model, expansion_ratio * d_model, **fc_kwargs)
-        self.act = nn.GELU(approximate='none')
-        self.down_proj = FC_CLASS_REGISTRY[fc_type](expansion_ratio * d_model, d_model, **fc_kwargs)
+            self.fc_kwargs['device'] = device
+        self.up_proj = FC_CLASS_REGISTRY[fc_type](d_model, ffn_hidden_size, **self.fc_kwargs)
+        self.act = act_fn
+        self.down_proj = FC_CLASS_REGISTRY[fc_type](ffn_hidden_size, d_model, **self.fc_kwargs)
         self.down_proj._is_residual = True
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         return self.down_proj(self.act(self.up_proj(x)))
-FFN_CLASS_REGISTRY = {'mptmlp': MPTMLP}
+
+class MPTGLU(MPTMLP):
+
+    def __init__(self, d_model: int, expansion_ratio: Union[int, float], fc_type: str='torch', ffn_hidden_size: Optional[int]=None, act_fn: Callable[[torch.Tensor], torch.Tensor]=_DEFAULT_ACT_FN, device: Optional[str]=None, bias: bool=True):
+        super().__init__(d_model=d_model, expansion_ratio=expansion_ratio, fc_type=fc_type, ffn_hidden_size=ffn_hidden_size, act_fn=act_fn, device=device, bias=bias)
+        self.gate_proj = FC_CLASS_REGISTRY[fc_type](d_model, self.up_proj.out_features, **self.fc_kwargs)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.down_proj(self.act(self.gate_proj(x)) * self.up_proj(x))
+FFN_CLASS_REGISTRY = {'mptmlp': MPTMLP, 'mptglu': MPTGLU}
 if te is not None:
     te.LayerNormMLP._has_norm = True
     FFN_CLASS_REGISTRY['te_ln_mlp'] = te.LayerNormMLP
 
-def build_ffn(d_model: int, expansion_ratio: int, fc_type: str='torch', device: Optional[str]=None, **kwargs: Any) -> nn.Module:
+def build_ffn(d_model: int, expansion_ratio: Union[int, float], fc_type: str='torch', ffn_hidden_size: Optional[int]=None, ffn_act_fn: Optional[dict]=None, device: Optional[str]=None, bias: bool=True, **kwargs: Any) -> nn.Module:
     ffn_type = kwargs.pop('ffn_type')
-    if ffn_type == 'mptmlp':
+    if ffn_type in ['mptmlp', 'mptglu']:
         if len(kwargs) > 0:
-            raise ValueError(f'MPTMLP got an unexpected keyword argument: {kwargs}')
-        return MPTMLP(d_model=d_model, expansion_ratio=expansion_ratio, fc_type=fc_type, device=device)
+            raise ValueError(f'MPTMLP (or MPTGLU) got an unexpected keyword argument: {kwargs}')
+        return FFN_CLASS_REGISTRY[ffn_type](d_model=d_model, expansion_ratio=expansion_ratio, fc_type=fc_type, act_fn=resolve_ffn_act_fn(ffn_act_fn), ffn_hidden_size=ffn_hidden_size, device=device, bias=bias)
     elif ffn_type == 'te_ln_mlp':
         assert te is not None
-        return te.LayerNormMLP(hidden_size=d_model, ffn_hidden_size=d_model * expansion_ratio, **kwargs)
+        ffn_hidden_size = resolve_ffn_hidden_size(d_model, expansion_ratio, ffn_hidden_size)
+        if ffn_act_fn is not None:
+            raise ValueError(f'Transformer Engine block does not support custom activation functions.')
+        return te.LayerNormMLP(hidden_size=d_model, ffn_hidden_size=ffn_hidden_size, bias=bias, **kwargs)
     raise ValueError(f'ffn_type={ffn_type!r} not recognized.')

hf_prefixlm_converter.py

@@ -6,23 +6,13 @@ Causal LM to convert it to a Prefix LM.
 Prefix LMs accepts a `bidirectional_mask` input in `forward`
 and treat the input prompt as the prefix in `generate`.
 """
-import math
-import warnings
 from types import MethodType
 from typing import Any, List, MutableMapping, Optional, Tuple, Union
 import torch
-from transformers.models.bloom.modeling_bloom import BaseModelOutputWithPastAndCrossAttentions, BloomForCausalLM, BloomModel, CausalLMOutputWithCrossAttentions, CrossEntropyLoss
-from transformers.models.bloom.modeling_bloom import _expand_mask as _expand_mask_bloom
-from transformers.models.bloom.modeling_bloom import _make_causal_mask as _make_causal_mask_bloom
-from transformers.models.bloom.modeling_bloom import logging
 from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
 from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM
 from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM
 from transformers.models.gptj.modeling_gptj import GPTJForCausalLM
-from transformers.models.opt.modeling_opt import OPTForCausalLM
-from transformers.models.opt.modeling_opt import _expand_mask as _expand_mask_opt
-from transformers.models.opt.modeling_opt import _make_causal_mask as _make_causal_mask_opt
-logger = logging.get_logger(__name__)
 _SUPPORTED_GPT_MODELS = (GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM)
 CAUSAL_GPT_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM]
 
@@ -110,232 +100,8 @@ def _convert_gpt_causal_lm_to_prefix_lm(model: CAUSAL_GPT_TYPES) -> CAUSAL_GPT_T
     setattr(model, 'generate', MethodType(generate, model))
     setattr(model, '_prefix_lm_converted', True)
     return model
-
-def _convert_bloom_causal_lm_to_prefix_lm(model: BloomForCausalLM) -> BloomForCausalLM:
-    """Converts a BLOOM Causal LM to a Prefix LM.
-
-    Supported HuggingFace model classes:
-        - `BloomForCausalLM`
-
-    See `convert_hf_causal_lm_to_prefix_lm` for more details.
-    """
-    if hasattr(model, '_prefix_lm_converted'):
-        return model
-    assert isinstance(model, BloomForCausalLM)
-    assert model.config.add_cross_attention == False, 'Only supports BLOOM decoder-only models'
-
-    def _prepare_attn_mask(self: BloomModel, attention_mask: torch.Tensor, bidirectional_mask: Optional[torch.Tensor], input_shape: Tuple[int, int], past_key_values_length: int) -> torch.BoolTensor:
-        combined_attention_mask = None
-        device = attention_mask.device
-        (_, src_length) = input_shape
-        if src_length > 1:
-            combined_attention_mask = _make_causal_mask_bloom(input_shape, device=device, past_key_values_length=past_key_values_length)
-            if bidirectional_mask is not None:
-                assert attention_mask.shape == bidirectional_mask.shape
-                expanded_bidirectional_mask = _expand_mask_bloom(bidirectional_mask, tgt_length=src_length)
-                combined_attention_mask = torch.logical_and(combined_attention_mask, expanded_bidirectional_mask)
-        expanded_attn_mask = _expand_mask_bloom(attention_mask, tgt_length=src_length)
-        combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
-        return combined_attention_mask
-
-    def _build_alibi_tensor(self: BloomModel, batch_size: int, query_length: int, key_length: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
-        num_heads = self.config.n_head
-        closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
-        base = torch.tensor(2 ** (-2 ** (-(math.log2(closest_power_of_2) - 3))), device=device, dtype=torch.float32)
-        powers = torch.arange(1, 1 + closest_power_of_2, device=device, dtype=torch.int32)
-        slopes = torch.pow(base, powers)
-        if closest_power_of_2 != num_heads:
-            extra_base = torch.tensor(2 ** (-2 ** (-(math.log2(2 * closest_power_of_2) - 3))), device=device, dtype=torch.float32)
-            num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
-            extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=device, dtype=torch.int32)
-            slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
-        qa = torch.arange(query_length, device=device, dtype=torch.int32).view(-1, 1)
-        ka = torch.arange(key_length, device=device, dtype=torch.int32).view(1, -1)
-        diffs = qa - ka + key_length - query_length
-        diffs = -diffs.abs()
-        alibi = slopes.view(1, num_heads, 1, 1) * diffs.view(1, 1, query_length, key_length)
-        alibi = alibi.expand(batch_size, -1, -1, -1).reshape(-1, query_length, key_length)
-        return alibi.to(dtype)
-    KeyValueT = Tuple[torch.Tensor, torch.Tensor]
-
-    def transformer_forward(self: BloomModel, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments: Any) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
-        if deprecated_arguments.pop('position_ids', False) is not False:
-            warnings.warn('`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. ' + 'You can safely ignore passing `position_ids`.', FutureWarning)
-        if len(deprecated_arguments) > 0:
-            raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
-        elif input_ids is not None:
-            (batch_size, seq_length) = input_ids.shape
-        elif inputs_embeds is not None:
-            (batch_size, seq_length, _) = inputs_embeds.shape
-        else:
-            raise ValueError('You have to specify either input_ids or inputs_embeds')
-        if past_key_values is None:
-            past_key_values = tuple([None] * len(self.h))
-        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-        hidden_states = self.word_embeddings_layernorm(inputs_embeds)
-        presents = () if use_cache else None
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-        if past_key_values[0] is not None:
-            tmp = past_key_values[0][0]
-            past_key_values_length = tmp.shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
-        if attention_mask is None:
-            attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
-        else:
-            attention_mask = attention_mask.to(hidden_states.device)
-        alibi = self._build_alibi_tensor(batch_size=batch_size, query_length=seq_length, key_length=seq_length_with_past, dtype=hidden_states.dtype, device=hidden_states.device)
-        causal_mask = self._prepare_attn_mask(attention_mask, bidirectional_mask, input_shape=(batch_size, seq_length), past_key_values_length=past_key_values_length)
-        for (i, (block, layer_past)) in enumerate(zip(self.h, past_key_values)):
-            if output_hidden_states:
-                hst = (hidden_states,)
-                all_hidden_states = all_hidden_states + hst
-            if self.gradient_checkpointing and self.training:
-                if use_cache:
-                    logger.warning('`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...')
-                    use_cache = False
-
-                def create_custom_forward(module: torch.nn.Module):
-
-                    def custom_forward(*inputs: Any):
-                        return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
-                    return custom_forward
-                outputs = torch.utils.checkpoint.checkpoint(create_custom_forward(block), hidden_states, alibi, causal_mask, head_mask[i])
-            else:
-                outputs = block(hidden_states, layer_past=layer_past, attention_mask=causal_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, alibi=alibi)
-            hidden_states = outputs[0]
-            if use_cache is True:
-                presents = presents + (outputs[1],)
-            if output_attentions:
-                oa = (outputs[2 if use_cache else 1],)
-                all_self_attentions = all_self_attentions + oa
-        hidden_states = self.ln_f(hidden_states)
-        if output_hidden_states:
-            hst = (hidden_states,)
-            all_hidden_states = all_hidden_states + hst
-        if not return_dict:
-            return tuple((v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None))
-        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions)
-    setattr(model.transformer, '_prepare_attn_mask', MethodType(_prepare_attn_mask, model.transformer))
-    setattr(model.transformer, '_build_alibi_tensor', MethodType(_build_alibi_tensor, model.transformer))
-    setattr(model.transformer, 'forward', MethodType(transformer_forward, model.transformer))
-    KeyValueT = Tuple[torch.Tensor, torch.Tensor]
-
-    def forward(self: BloomForCausalLM, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.Tensor]=None, labels: Optional[torch.Tensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments: Any) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
-        """Replacement forward method for BloomCausalLM."""
-        if deprecated_arguments.pop('position_ids', False) is not False:
-            warnings.warn('`position_ids` have no functionality in BLOOM and will be removed ' + 'in v5.0.0. You can safely ignore passing `position_ids`.', FutureWarning)
-        if len(deprecated_arguments) > 0:
-            raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        transformer_outputs = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, bidirectional_mask=bidirectional_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
-        hidden_states = transformer_outputs[0]
-        lm_logits = self.lm_head(hidden_states)
-        loss = None
-        if labels is not None:
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            (batch_size, seq_length, vocab_size) = shift_logits.shape
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length))
-        if not return_dict:
-            output = (lm_logits,) + transformer_outputs[1:]
-            return (loss,) + output if loss is not None else output
-        return CausalLMOutputWithCrossAttentions(loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
-
-    def prepare_inputs_for_generation(self: BloomForCausalLM, input_ids: torch.LongTensor, past: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs: Any) -> dict:
-        del kwargs
-        if past:
-            input_ids = input_ids[:, -1].unsqueeze(-1)
-            bidirectional_mask = None
-            if past[0][0].shape[0] == input_ids.shape[0]:
-                past = self._convert_to_bloom_cache(past)
-        else:
-            bidirectional_mask = torch.ones_like(input_ids)
-        return {'input_ids': input_ids, 'past_key_values': past, 'use_cache': True, 'attention_mask': attention_mask, 'bidirectional_mask': bidirectional_mask}
-    setattr(model, 'forward', MethodType(forward, model))
-    setattr(model, 'prepare_inputs_for_generation', MethodType(prepare_inputs_for_generation, model))
-    setattr(model, '_prefix_lm_converted', True)
-    return model
-
-def _convert_opt_causal_lm_to_prefix_lm(model: OPTForCausalLM) -> OPTForCausalLM:
-    """Converts an OPT Causal LM to a Prefix LM.
-
-    Supported HuggingFace model classes:
-        - `OPTForCausalLM`
-
-    See `convert_hf_causal_lm_to_prefix_lm` for more details.
-    """
-    if hasattr(model, '_prefix_lm_converted'):
-        return model
-    assert isinstance(model, OPTForCausalLM)
-    assert model.config.add_cross_attention == False, 'Only supports OPT decoder-only models'
-    setattr(model, '_original_forward', getattr(model, 'forward'))
-    setattr(model, '_original_generate', getattr(model, 'generate'))
-    model.model.decoder.bidirectional_mask = None
-
-    def _prepare_decoder_attention_mask(self: torch.nn.Module, attention_mask: Optional[torch.Tensor], input_shape: Tuple[int, int], inputs_embeds: Optional[torch.Tensor], past_key_values_length: int):
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            assert inputs_embeds is not None
-            if self.bidirectional_mask == 'g':
-                (bsz, src_length) = input_shape
-                combined_attention_mask = torch.zeros((bsz, 1, src_length, src_length + past_key_values_length), dtype=inputs_embeds.dtype, device=inputs_embeds.device)
-            else:
-                combined_attention_mask = _make_causal_mask_opt(input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length).to(inputs_embeds.device)
-                if self.bidirectional_mask is not None:
-                    assert attention_mask is not None
-                    assert attention_mask.shape == self.bidirectional_mask.shape
-                    expanded_bidirectional_mask = _expand_mask_opt(self.bidirectional_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)
-                    combined_attention_mask = torch.maximum(expanded_bidirectional_mask, combined_attention_mask)
-        if attention_mask is not None:
-            assert inputs_embeds is not None
-            expanded_attn_mask = _expand_mask_opt(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)
-            combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-        return combined_attention_mask
-    setattr(model.model.decoder, '_prepare_decoder_attention_mask', MethodType(_prepare_decoder_attention_mask, model.model.decoder))
-
-    def forward(self: OPTForCausalLM, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.ByteTensor]=None, head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[List[torch.FloatTensor]]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):
-
-        def call_og_forward():
-            return self._original_forward(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
-        if bidirectional_mask is None:
-            return call_og_forward()
-        self.model.decoder.bidirectional_mask = bidirectional_mask
-        try:
-            outputs = call_og_forward()
-        except:
-            self.model.decoder.bidirectional_mask = None
-            raise
-        self.model.decoder.bidirectional_mask = None
-        return outputs
-
-    def generate(self: OPTForCausalLM, *args: tuple, **kwargs: Any):
-        """Wraps original generate to enable PrefixLM-style attention."""
-        self.model.decoder.bidirectional_mask = 'g'
-        try:
-            output = self._original_generate(*args, **kwargs)
-        except:
-            self.model.decoder.bidirectional_mask = None
-            raise
-        self.model.decoder.bidirectional_mask = None
-        return output
-    setattr(model, 'forward', MethodType(forward, model))
-    setattr(model, 'generate', MethodType(generate, model))
-    setattr(model, '_prefix_lm_converted', True)
-    return model
-_SUPPORTED_HF_MODELS = _SUPPORTED_GPT_MODELS + (BloomForCausalLM, OPTForCausalLM)
-CAUSAL_LM_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM, BloomForCausalLM, OPTForCausalLM]
+_SUPPORTED_HF_MODELS = _SUPPORTED_GPT_MODELS
+CAUSAL_LM_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM]
 
 def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES:
     """Converts a HuggingFace Causal LM to a Prefix LM.
@@ -345,8 +111,6 @@ def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES
         - `GPTNeoForCausalLM`
         - `GPTNeoXForCausalLM`
         - `GPTJForCausalLM`
-        - `BloomForCausalLM`
-        - `OPTForCausalLM`
 
     Conversion to a Prefix LM is done by modifying the `forward` method, and possibly also the
     `generate` method and/or select underlying methods depending on the model class.
@@ -396,10 +160,6 @@ def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES
     """
     if isinstance(model, _SUPPORTED_GPT_MODELS):
         return _convert_gpt_causal_lm_to_prefix_lm(model)
-    elif isinstance(model, BloomForCausalLM):
-        return _convert_bloom_causal_lm_to_prefix_lm(model)
-    elif isinstance(model, OPTForCausalLM):
-        return _convert_opt_causal_lm_to_prefix_lm(model)
     else:
         raise TypeError(f'Cannot convert model to Prefix LM. ' + f'Model does not belong to set of supported HF models:' + f'\n{_SUPPORTED_HF_MODELS}')
 

modeling_mpt.py

@@ -8,9 +8,18 @@ from typing import Any, Dict, List, Mapping, MutableMapping, Optional, Tuple, Un
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from .attention import is_flash_v2_installed
+if is_flash_v2_installed():
+    try:
+        from flash_attn.layers.rotary import RotaryEmbedding as DAILRotaryEmbedding
+    except Exception as e:
+        raise e
 from transformers import PreTrainedModel, PreTrainedTokenizerBase
 from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from .attention import attn_bias_shape, build_attn_bias
+from transformers.models.llama.modeling_llama import LlamaDynamicNTKScalingRotaryEmbedding as HFDynamicNTKScalingRotaryEmbedding
+from transformers.models.llama.modeling_llama import LlamaLinearScalingRotaryEmbedding as HFLinearScalingRotaryEmbedding
+from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding as HFRotaryEmbedding
+from .attention import ATTN_CLASS_REGISTRY, attn_bias_shape, build_attn_bias, gen_slopes
 from .blocks import MPTBlock
 from .custom_embedding import SharedEmbedding
 from .fc import FC_CLASS_REGISTRY as FC_CLASS_REGISTRY
@@ -30,6 +39,96 @@ except:
 import logging
 log = logging.getLogger(__name__)
 
+def gen_rotary_embedding(rope_head_dim: int, rope_impl: str, rope_theta: int, rope_dail_config: dict, rope_hf_config: dict, max_seq_len: int):
+    if rope_impl == 'dail':
+        return DAILRotaryEmbedding(dim=rope_head_dim, base=rope_theta, interleaved=False, scale_base=rope_dail_config['xpos_scale_base'] if rope_dail_config['type'] == 'xpos' else None, pos_idx_in_fp32=rope_dail_config['pos_idx_in_fp32'], device='cpu')
+    elif rope_impl == 'hf':
+        if rope_hf_config['type'] == 'no_scaling':
+            return HFRotaryEmbedding(rope_head_dim, max_position_embeddings=max_seq_len, base=rope_theta, device='cpu')
+        elif rope_hf_config['type'] == 'linear':
+            return HFLinearScalingRotaryEmbedding(rope_head_dim, max_position_embeddings=max_seq_len, base=rope_theta, scaling_factor=rope_hf_config['factor'], device='cpu')
+        elif rope_hf_config['type'] == 'dynamic':
+            return HFDynamicNTKScalingRotaryEmbedding(rope_head_dim, max_position_embeddings=max_seq_len, base=rope_theta, scaling_factor=rope_hf_config['factor'], device='cpu')
+    raise ValueError('rope_impl needs to be either dail or hf')
+
+def gen_attention_mask_in_length(sequence_id: Union[None, torch.Tensor], S: int, attn_uses_sequence_id: bool, attn_impl: str, attention_mask: Union[torch.Tensor, None]):
+    """Generates the attention mask used for sequence masking in FA v2.
+
+    Only supports sequence id based sparse attention for no attention masking or attention masking with right padding.
+    In case of left padding:
+        1. Training with left padding is not supported in MPT (see https://github.com/mosaicml/llm-foundry/blob/1eecd4cb8e734499f77f6a35f657b8b20c0adfcb/llmfoundry/models/mpt/modeling_mpt.py#L407).
+        2. For generation with left padding, we only have a single sequence id per sample, so we don't need sequence id based sparse attention.
+
+    Args:
+        sequence_id (Union[None, torch.Tensor]): Tensor containing the sequence id for each token. Shape (batch_size, seq_len).
+        S (int): Sequence length
+        attn_uses_sequence_id (bool): Whether the attention uses sequence id based masking.
+        attn_impl (str): Attention implementation. This function is only creates attention_mask_in_length for flash attention.
+        attention_mask (Union[torch.Tensor, None]): Attention mask tensor of shape (batch_size, seq_len)
+
+    Returns:
+        attention_mask_in_length: (batch, seqlen), int, a nonzero number (e.g., 1, 2, 3, etc.) means length of concatenated sequence in b-th batch, and 0 means none. For example, if batch = 3 and seqlen = 6, the attention_mask_in_length is:
+            ```
+            [
+            [2, 3, 0, 0, 0, 0],
+            [3, 2, 0, 0, 0, 0],
+            [6, 0, 0, 0, 0, 0]
+            ]
+            ```
+        , which refers to the 3D-attention mask:
+            ```
+            [
+            [
+                [1, 0, 0, 0, 0, 0],
+                [1, 1, 0, 0, 0, 0],
+                [0, 0, 1, 0, 0, 0],
+                [0, 0, 1, 1, 0, 0],
+                [0, 0, 1, 1, 1, 0],
+                [0, 0, 0, 0, 0, 1]
+            ],
+            [
+                [1, 0, 0, 0, 0, 0],
+                [1, 1, 0, 0, 0, 0],
+                [1, 1, 1, 0, 0, 0],
+                [0, 0, 0, 1, 0, 0],
+                [0, 0, 0, 1, 1, 0],
+                [0, 0, 0, 0, 0, 1]
+            ],
+            [
+                [1, 0, 0, 0, 0, 0],
+                [1, 1, 0, 0, 0, 0],
+                [1, 1, 1, 0, 0, 0],
+                [1, 1, 1, 1, 0, 0],
+                [1, 1, 1, 1, 1, 0],
+                [1, 1, 1, 1, 1, 1]
+            ]
+            ]
+            ```.
+            (The description above is taken verbatim from https://github.com/Dao-AILab/flash-attention/blob/9356a1c0389660d7e231ff3163c1ac17d9e3824a/flash_attn/bert_padding.py#L125 .)
+    """
+    attention_mask_in_length = None
+    if sequence_id is not None and attn_uses_sequence_id and (attn_impl == 'flash'):
+        if attention_mask is not None and attention_mask[:, 0].sum() != attention_mask.shape[0]:
+            raise NotImplementedError('Left padding is not supported with flash attention when attn_uses_sequence_id is set to True.')
+        if S != sequence_id.shape[-1]:
+            raise ValueError(f'Sequence length ({S}) does not match length of sequences in sequence_id ({sequence_id.shape[-1]}).')
+        attention_mask_in_length = torch.nn.functional.one_hot(sequence_id)
+        if attention_mask is not None:
+            attention_mask_in_length = attention_mask_in_length.masked_fill(~attention_mask.unsqueeze(-1), 0)
+        attention_mask_in_length = attention_mask_in_length.sum(dim=1)
+        attention_mask_in_length = torch.nn.functional.pad(attention_mask_in_length, (0, S - attention_mask_in_length.shape[-1]), mode='constant', value=0)
+    return attention_mask_in_length
+
+def apply_sequence_id(attn_bias: torch.Tensor, sequence_id: torch.LongTensor, max_seq_len: int) -> torch.Tensor:
+    seq_len = sequence_id.shape[-1]
+    if seq_len > max_seq_len:
+        raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={max_seq_len}')
+    attn_bias = attn_bias[..., :seq_len, :seq_len]
+    cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1)
+    min_val = torch.finfo(attn_bias.dtype).min
+    attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
+    return attn_bias
+
 class MPTPreTrainedModel(PreTrainedModel):
     config_class = MPTConfig
     base_model_prefix = 'model'
@@ -62,6 +161,11 @@ class MPTModel(MPTPreTrainedModel):
         self.emb_drop = nn.Dropout(config.emb_pdrop)
         self.blocks = nn.ModuleList([MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers)])
         self.norm_f = norm_class(config.d_model, device=config.init_device)
+        self.rope = config.attn_config['rope']
+        self.rope_impl = None
+        if self.rope:
+            self.rope_impl = config.attn_config['rope_impl']
+            self.rotary_embedding = gen_rotary_embedding(rope_head_dim=config.d_model // config.n_heads, rope_impl=self.rope_impl, rope_theta=config.attn_config['rope_theta'], rope_dail_config=config.attn_config['rope_dail_config'], rope_hf_config=config.attn_config['rope_hf_config'], max_seq_len=self.config.max_seq_len)
         if config.init_device != 'meta':
             log.info(f'We recommend using config.init_device="meta" with Composer + FSDP for faster initialization.')
             self.apply(self.param_init_fn)
@@ -74,13 +178,16 @@ class MPTModel(MPTPreTrainedModel):
                 if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter):
                     log.info(f'Removing bias ({module.bias}) from {module}.')
                     module.register_parameter('bias', None)
+                if hasattr(module, 'use_bias'):
+                    log.info(f'Setting use_bias=False for {module}.')
+                    module.use_bias = False
         log.debug(self)
         log.debug(f"Using {self.config.init_config['name']} initialization.")
 
-    def get_input_embeddings(self) -> nn.Embedding:
+    def get_input_embeddings(self) -> Union[SharedEmbedding, nn.Embedding]:
         return self.wte
 
-    def set_input_embeddings(self, value: nn.Embedding) -> None:
+    def set_input_embeddings(self, value: Union[SharedEmbedding, nn.Embedding]) -> None:
         self.wte = value
 
     @torch.no_grad()
@@ -101,7 +208,7 @@ class MPTModel(MPTPreTrainedModel):
             attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)
         if self.attn_uses_sequence_id and sequence_id is not None:
             assert isinstance(attn_bias, torch.Tensor)
-            attn_bias = self._apply_sequence_id(attn_bias, sequence_id)
+            attn_bias = apply_sequence_id(attn_bias, sequence_id, self.config.max_seq_len)
         if attention_mask is not None:
             s_k = attention_mask.shape[-1]
             if attn_bias is None:
@@ -113,7 +220,7 @@ class MPTModel(MPTPreTrainedModel):
                 raise ValueError(f'attention_mask shape={attention_mask.shape} ' + f'and prefix_mask shape={prefix_mask.shape} are not equal.')
             min_val = torch.finfo(attn_bias.dtype).min
             attn_bias = attn_bias.masked_fill(~attention_mask.view(-1, 1, 1, s_k), min_val)
-        return (attn_bias, None)
+        return (attn_bias, attention_mask)
 
     def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor) -> torch.Tensor:
         (s_k, s_q) = attn_bias.shape[-2:]
@@ -130,17 +237,7 @@ class MPTModel(MPTPreTrainedModel):
         attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
         return attn_bias
 
-    def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor) -> torch.Tensor:
-        seq_len = sequence_id.shape[-1]
-        if seq_len > self.config.max_seq_len:
-            raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
-        attn_bias = attn_bias[..., :seq_len, :seq_len]
-        cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1)
-        min_val = torch.finfo(attn_bias.dtype).min
-        attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
-        return attn_bias
-
-    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.Tensor]=None) -> BaseModelOutputWithPast:
+    def forward(self, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.Tensor]=None) -> BaseModelOutputWithPast:
         return_dict = return_dict if return_dict is not None else self.config.return_dict
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         if attention_mask is not None:
@@ -156,17 +253,26 @@ class MPTModel(MPTPreTrainedModel):
             raise NotImplementedError('MPT does not support training with left padding.')
         if self.prefix_lm and prefix_mask is None:
             raise ValueError('prefix_mask is a required argument when MPT is configured with prefix_lm=True.')
-        if inputs_embeds is not None:
-            raise NotImplementedError('inputs_embeds is not implemented for MPT.')
         if self.training:
             if self.attn_uses_sequence_id and sequence_id is None:
                 raise ValueError('sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True ' + 'and the model is in train mode.')
             elif self.attn_uses_sequence_id is False and sequence_id is not None:
                 warnings.warn('MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. ' + 'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.')
-        S = input_ids.size(1)
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError('You cannot specify both input_ids and inputs_embeds.')
+        elif input_ids is not None:
+            S = input_ids.size(1)
+            x = self.wte(input_ids)
+            input_device = input_ids.device
+        elif inputs_embeds is not None:
+            S = inputs_embeds.size(1)
+            x = inputs_embeds
+            input_device = inputs_embeds.device
+        else:
+            raise ValueError('You must specify input_ids or inputs_embeds')
         assert S <= self.config.max_seq_len, f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}'
-        tok_emb = self.wte(input_ids)
-        if self.learned_pos_emb:
+        rotary_emb_w_meta_info = None
+        if self.learned_pos_emb or self.rope:
             past_position = 0
             if past_key_values is not None:
                 if len(past_key_values) != self.config.n_layers:
@@ -174,15 +280,18 @@ class MPTModel(MPTPreTrainedModel):
                 past_position = past_key_values[0][0].size(1)
                 if self.attn_impl == 'torch':
                     past_position = past_key_values[0][0].size(3)
-            if S + past_position > self.config.max_seq_len:
+            if self.learned_pos_emb and S + past_position > self.config.max_seq_len:
                 raise ValueError(f'Cannot forward input with past sequence length {past_position} and current sequence length ' + f'{S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.')
-            pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_ids.device).unsqueeze(0)
-            if attention_mask is not None:
-                pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0)
-            pos_emb = self.wpe(pos)
-            x = tok_emb + pos_emb
-        else:
-            x = tok_emb
+            if self.learned_pos_emb or (self.rope and self.rope_impl == 'hf'):
+                pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_device).unsqueeze(0)
+                if attention_mask is not None:
+                    pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0)
+                if self.learned_pos_emb:
+                    x = x + self.wpe(pos)
+                elif self.rope and self.rope_impl == 'hf':
+                    rotary_emb_w_meta_info = {'impl': self.rope_impl, 'rotary_emb': self.rotary_embedding, 'offset_info': pos, 'seq_len': S + past_position}
+            elif self.rope and self.rope_impl == 'dail':
+                rotary_emb_w_meta_info = {'impl': self.rope_impl, 'rotary_emb': self.rotary_embedding, 'offset_info': past_position, 'seq_len': S + past_position}
         if self.embedding_fraction == 1:
             x = self.emb_drop(x)
         else:
@@ -190,6 +299,11 @@ class MPTModel(MPTPreTrainedModel):
             assert isinstance(self.emb_drop, nn.Module)
             x = self.emb_drop(x_shrunk)
         (attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)
+        attention_mask_in_length = gen_attention_mask_in_length(sequence_id=sequence_id, S=S, attn_uses_sequence_id=self.attn_uses_sequence_id, attn_impl=self.attn_impl, attention_mask=attention_mask)
+        alibi_slopes = None
+        if self.alibi and self.attn_impl == 'flash':
+            alibi_slopes = gen_slopes(n_heads=self.config.n_heads, alibi_bias_max=self.alibi_bias_max, device=x.device, return_1d=True)
+        presents = () if use_cache else None
         if use_cache and past_key_values is None:
             past_key_values = [() for _ in range(self.config.n_layers)]
         all_hidden_states = () if output_hidden_states else None
@@ -199,9 +313,9 @@ class MPTModel(MPTPreTrainedModel):
                 assert all_hidden_states is not None
                 all_hidden_states = all_hidden_states + (x,)
             past_key_value = past_key_values[b_idx] if past_key_values is not None else None
-            (x, attn_weights, past_key_value) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal)
-            if past_key_values is not None:
-                past_key_values[b_idx] = past_key_value
+            (x, attn_weights, present) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, rotary_emb_w_meta_info=rotary_emb_w_meta_info, attention_mask=attention_mask, is_causal=self.is_causal, output_attentions=bool(output_attentions), attention_mask_in_length=attention_mask_in_length, alibi_slopes=alibi_slopes)
+            if presents is not None:
+                presents += (present,)
             if output_attentions:
                 assert all_self_attns is not None
                 all_self_attns = all_self_attns + (attn_weights,)
@@ -209,7 +323,7 @@ class MPTModel(MPTPreTrainedModel):
         if output_hidden_states:
             assert all_hidden_states is not None
             all_hidden_states = all_hidden_states + (x,)
-        return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns)
+        return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attns)
 
     def param_init_fn(self, module: nn.Module) -> None:
         init_fn_name = self.config.init_config['name']
@@ -225,10 +339,12 @@ class MPTForCausalLM(MPTPreTrainedModel):
 
     def __init__(self, config: MPTConfig):
         super().__init__(config)
-        if not config.tie_word_embeddings:
-            raise ValueError('MPTForCausalLM only supports tied word embeddings')
         log.info(f'Instantiating an MPTForCausalLM model from {__file__}')
         self.transformer: MPTModel = MPTModel(config)
+        self.lm_head = None
+        if not config.tie_word_embeddings:
+            self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False, device=config.init_device)
+            self.lm_head._fsdp_wrap = True
         for child in self.transformer.children():
             if isinstance(child, torch.nn.ModuleList):
                 continue
@@ -244,17 +360,28 @@ class MPTForCausalLM(MPTPreTrainedModel):
                     raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
             self.logit_scale = logit_scale
 
-    def get_input_embeddings(self) -> nn.Embedding:
-        return self.transformer.wte
+    def get_input_embeddings(self) -> Union[SharedEmbedding, nn.Embedding]:
+        return self.transformer.get_input_embeddings()
 
     def set_input_embeddings(self, value: Union[SharedEmbedding, nn.Embedding]) -> None:
-        self.transformer.wte = value
+        self.transformer.set_input_embeddings(value)
 
-    def get_output_embeddings(self) -> nn.Embedding:
-        return self.transformer.wte
+    def get_output_embeddings(self) -> Union[SharedEmbedding, nn.Embedding, nn.Linear]:
+        if self.lm_head is not None:
+            return self.lm_head
+        return self.transformer.get_input_embeddings()
 
-    def set_output_embeddings(self, new_embeddings: Union[SharedEmbedding, nn.Embedding]) -> None:
-        self.transformer.wte = new_embeddings
+    def set_output_embeddings(self, new_embeddings: Union[SharedEmbedding, nn.Embedding, nn.Linear]) -> None:
+        if self.lm_head is not None:
+            self.lm_head = new_embeddings
+        else:
+            if not isinstance(new_embeddings, (SharedEmbedding, nn.Embedding)):
+                raise ValueError('new_embeddings must be an instance of SharedEmbedding ' + f'or nn.Embedding, but got {type(new_embeddings)}.')
+            warnings.warn('Using `set_output_embeddings` to set the embedding layer of ' + 'MPTForCausalLM with tied weights. Given weights are tied, ' + 'using `set_input_embeddings` is recommended over using ' + '`set_output_embeddings`.')
+            self.transformer.set_input_embeddings(new_embeddings)
+
+    def tie_weights(self) -> None:
+        self.lm_head = None
 
     def set_decoder(self, decoder: MPTModel) -> None:
         self.transformer = decoder
@@ -262,13 +389,16 @@ class MPTForCausalLM(MPTPreTrainedModel):
     def get_decoder(self) -> MPTModel:
         return self.transformer
 
-    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor]=None) -> CausalLMOutputWithPast:
+    def forward(self, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor]=None) -> CausalLMOutputWithPast:
         return_dict = return_dict if return_dict is not None else self.config.return_dict
         use_cache = use_cache if use_cache is not None else self.config.use_cache
-        if inputs_embeds is not None:
-            raise NotImplementedError('inputs_embeds has to be None (for hf/peft support).')
-        outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache)
-        logits = self.transformer.wte(outputs.last_hidden_state.to(self.transformer.wte.weight.device), True)
+        outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache, inputs_embeds=inputs_embeds)
+        if self.lm_head is not None:
+            logits = self.lm_head(outputs.last_hidden_state)
+        else:
+            out = outputs.last_hidden_state
+            out = out.to(self.transformer.wte.weight.device)
+            logits = self.transformer.wte(out, True)
         if self.logit_scale is not None:
             if self.logit_scale == 0:
                 warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')
@@ -288,11 +418,31 @@ class MPTForCausalLM(MPTPreTrainedModel):
         return isinstance(module, MPTBlock)
 
     def activation_checkpointing_fn(self, module: nn.Module) -> bool:
-        return isinstance(module, MPTBlock)
+        act_ckpt_list = getattr(self.config, 'activation_checkpointing_target', None) or ['MPTBlock']
+        if isinstance(act_ckpt_list, str):
+            act_ckpt_list = [act_ckpt_list]
+        elif not isinstance(act_ckpt_list, list):
+            raise ValueError(f'activation_checkpointing_target must be either a single string or a list, but got {type(act_ckpt_list)}')
+        if 'MPTBlock' in act_ckpt_list or 'mptblock' in act_ckpt_list:
+            if len(act_ckpt_list) > 1:
+                log.info('Activation checkpointing MPTBlock only (ignoring other sub-block modules specified in activation_checkpointing_target).')
+            return isinstance(module, MPTBlock)
+        mod_types = ()
+        for mod_name in act_ckpt_list:
+            if mod_name.lower() == 'mptblock':
+                mod_types += (MPTBlock,)
+            elif mod_name in ATTN_CLASS_REGISTRY:
+                mod_types += (ATTN_CLASS_REGISTRY[mod_name],)
+            elif mod_name in FFN_CLASS_REGISTRY:
+                mod_types += (FFN_CLASS_REGISTRY[mod_name],)
+            elif mod_name in NORM_CLASS_REGISTRY:
+                mod_types += (NORM_CLASS_REGISTRY[mod_name],)
+            else:
+                msg = ', '.join(list(ATTN_CLASS_REGISTRY.keys()) + list(FFN_CLASS_REGISTRY.keys()) + list(NORM_CLASS_REGISTRY.keys()) + ['MPTBlock'])
+                raise ValueError(f'{mod_name} (specified in activation_checkpointing_target) is not a recognized option out of available options {msg}.')
+        return isinstance(module, mod_types)
 
     def prepare_inputs_for_generation(self, input_ids: torch.Tensor, past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]]=None, inputs_embeds: Optional[torch.Tensor]=None, **kwargs: Any) -> Dict[str, Any]:
-        if inputs_embeds is not None:
-            raise NotImplementedError('inputs_embeds is not implemented for MPT yet')
         attention_mask = kwargs['attention_mask'].bool()
         if attention_mask[:, -1].sum() != attention_mask.shape[0]:
             raise NotImplementedError('MPT does not support generation with right padding.')
@@ -308,7 +458,12 @@ class MPTForCausalLM(MPTPreTrainedModel):
                 raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.')
         else:
             prefix_mask = None
-        return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True)}
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {'inputs_embeds': inputs_embeds}
+        else:
+            model_inputs = {'input_ids': input_ids}
+        model_inputs.update({'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True)})
+        return model_inputs
 
     @staticmethod
     def _reorder_cache(past_key_values: List[Tuple[torch.Tensor, torch.Tensor]], beam_idx: torch.LongTensor) -> List[Tuple[torch.Tensor, ...]]: